






"""simple docstring"""





import copy
import os

import cva
import numpy as np
from matplotlib import pyplot as plt






class 	a		:






    def __init__(			self	:				Optional[int]   )	->						str:
      lowerCamelCase_       	=		''
      lowerCamelCase_       	=		''
      lowerCamelCase_       	=		[]
      lowerCamelCase_       	=		0
      lowerCamelCase_       	=		256
      lowerCamelCase_       	=		0
      lowerCamelCase_       	=		0
      lowerCamelCase_       	=		0
      lowerCamelCase_       	=		0






    def   UpperCamelCase			(			self	:				int			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]   )	->						Tuple:
      lowerCamelCase_       	=		cva.imread(__SCREAMING_SNAKE_CASE			,		0   )
      lowerCamelCase_       	=		copy.deepcopy(self.img   )
      lowerCamelCase_	,      lowerCamelCase_	,      lowerCamelCase_       	=		plt.hist(self.img.ravel()			,		256			,		[0, 256]			,		label='x'   )
      lowerCamelCase_       	=		np.sum(__SCREAMING_SNAKE_CASE   )
      for i in range(len(__SCREAMING_SNAKE_CASE   )   ):
        lowerCamelCase_       	=		x[i] / self.k
        self.sk += prk
        lowerCamelCase_       	=		(self.L - 1) * self.sk
        if self.rem != 0:
          lowerCamelCase_       	=		int(last % last   )
        lowerCamelCase_       	=		int(last + 1 if self.rem >= 0.5 else last   )
        self.last_list.append(__SCREAMING_SNAKE_CASE   )
        lowerCamelCase_       	=		int(np.ma.count(self.img   ) / self.img[1].size   )
        lowerCamelCase_       	=		self.img[1].size
      for i in range(self.number_of_cols   ):
        for j in range(self.number_of_rows   ):
          lowerCamelCase_       	=		self.img[j][i]
          if num != self.last_list[num]:
            lowerCamelCase_       	=		self.last_list[num]
      cva.imwrite('output_data/output.jpg'			,		self.img   )






    def   UpperCamelCase			(			self	:				Tuple   )	->						str:
      plt.hist(self.img.ravel()			,		256			,		[0, 256]   )






    def   UpperCamelCase			(			self	:				str   )	->						str:
      cva.imshow('Output-Image'			,		self.img   )
      cva.imshow('Input-Image'			,		self.original_image   )
      cva.waitKey(5000   )
      cva.destroyAllWindows()


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE   :							Tuple          =		os.path.join(os.path.basename(__file__), '''image_data/input.jpg''')
  _SCREAMING_SNAKE_CASE   :							Any          =		ConstantStretch()
  stretcher.stretch(file_path)
  stretcher.plot_histogram()
  stretcher.show_image()












"""simple docstring"""





import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures

# Importing the dataset
_SCREAMING_SNAKE_CASE   :							int          =		pd.read_csv(
    '''https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/'''
    '''position_salaries.csv'''
)
_SCREAMING_SNAKE_CASE   :							List[Any]          =		dataset.iloc[:, 1:2].values
_SCREAMING_SNAKE_CASE   :							str          =		dataset.iloc[:, 2].values


_SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE   :							Dict          =		train_test_split(X, y, test_size=0.2, random_state=0)


_SCREAMING_SNAKE_CASE   :							Optional[int]          =		PolynomialFeatures(degree=4)
_SCREAMING_SNAKE_CASE   :							Union[str, Any]          =		poly_reg.fit_transform(X)
_SCREAMING_SNAKE_CASE   :							List[str]          =		LinearRegression()
pol_reg.fit(X_poly, y)







def 	lowerCamelCase__			(				) ->							List[str]:
  plt.scatter(_lowerCamelCase						,							_lowerCamelCase						,							color='red'					)
  plt.plot(_lowerCamelCase						,							pol_reg.predict(poly_reg.fit_transform(_lowerCamelCase					)					)						,							color='blue'					)
  plt.title('Truth or Bluff (Linear Regression)'					)
  plt.xlabel('Position level'					)
  plt.ylabel('Salary'					)
  plt.show()


if __name__ == "__main__":
  viz_polymonial()

  # Predicting a new result with Polymonial Regression
  pol_reg.predict(poly_reg.fit_transform([[5.5]]))
  # output should be 132148.43750003




